vmestimator is a new open-source tool that estimates metric cardinality in real time. It can alert to high-cardinality spikes before they slow queries and increase resource usage. Works with any Prometheus-compatible setups, Grafana, and vmalert
VictoriaMetrics is a fast, scalable monitoring system made of modular components like vminsert, vmstorage, and vmselect. It supports both single-node and clustered setups, along with tools for backup, restore, alerting, access control, and data migration. Data can be ingested, stored, queried, backed up, and restored with high performance and minimal resource use.
Recording rules can transform complex PromQL queries into pre-computed metrics, eliminating repeated calculation overhead. When organized in groups, these alerting and recording rules can be precisely scheduled to run at specific times like 01:30, 02:30, and even execute in parallel for maximum efficiency.
The article explains vmselect’s core functionalities: concurrent request processing, query parsing and validation, data fetching and merging from vmstorage nodes, and memory-efficient result caching.
OpenTelemetry, Prometheus 2.x, Prometheus 3.x, and vmagent are put together for comparison in scraping and pushing data to remote storage.
When vmselect asks vmstorage for data, there’s actually quite a bit happening behind the scenes. This article breaks down the process of how vmstorage locates and retrieves your metrics, from finding the right TSIDs to sending back the actual data points.
IndexDB acts as vmstorage’s memory - it remembers which numbers (TSIDs) belong to which metrics, making sure your queries get answered fast. This article walks through how this system works, from the way it organizes data to how it keeps track of millions of timeseries.
vmstorage takes data from vminsert through a concurrency limiter, creates TSIDs for each row, and puts them in memory buffers. Every few seconds, it moves data to storage parts on disk. The system merges parts, removes duplicates, and cleans old data. This turns raw metrics into data users can search.
This article explains how vmstorage processes incoming metrics, assigns unique IDs to timeseries, and organizes everything into different types of storage parts. The whole system is pretty clever - it uses in-memory buffers for speed, smart compression to save space, and has various watchdogs keeping an eye on things like disk space and data retention.
Time series databases are essential tools in any software engineer’s toolbelt. Their development has been shaped by user needs and countless open source contributors, leading to the healthy ecosystem of options we see today. In this article, you’ll see how time series databases came about, and why so many are open source.